Modelling immune systems responses for the development of energy sharing strategies for swarm robotic systems

机译：建模免疫系统响应，以开发群体机器人系统的能量共享策略

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This paper presents an initial investigation on studying an immune systems response, the granuloma formation, for inspirations on the development of energy sharing strategies for swarm robotic systems. Granuloma formation is a process in which unwanted substances are removed by immune systems. To better understand the components and the processes in granuloma formation, we modeled them using Unified Modelling Language (UML). Based on this model, analogous properties of the granuloma formation and swarm robotic systems are mapped accordingly. Based on these work, energy sharing strategies which are inspired by the process in granuloma formation are proposed for fault tolerance in swarm robotic systems.

机译：本文提出了一项初步研究，旨在研究免疫系统反应，肉芽肿的形成，以期为开发群体机器人系统的能量共享策略提供灵感。肉芽肿的形成是通过免疫系统去除不需要的物质的过程。为了更好地了解肉芽肿形成的组成部分和过程，我们使用统一建模语言（UML）对它们进行了建模。在此模型的基础上，相应地映射了肉芽肿形成和群体机器人系统的类似属性。基于这些工作，提出了受肉芽肿形成过程启发的能量共享策略，以用于群体机器人系统的容错。

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《International Conference on Computational Science and Technology》|2014年|1-6|共6页
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Al Haek Mohammed; Ismail Amelia Ritahani; Nordin Azlin; Sulaiman Suriani; HuiKeng Lau;
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Unified Modeling Language; artificial immune systems; multi-robot systems; UML; analogous properties; energy sharing strategies; fault tolerance; granuloma formation; immune system response modelling; swarm robotic systems; unified modelling language; Computational modeling; Lymph nodes; Microorganisms; Robot kinematics; Unified modeling language; Artificial Immune Systems; Immune Process Modelling; Modelling; Swarm Robotics;

机译：统一建模语言;人工免疫系统;多机器人系统; UML;模拟特性;能量共享策略;容错;肉芽肿形成;免疫系统响应建模;群体机器人系统;统一建模语言;计算建模;淋巴结;微生物;机器人运动学;统一建模语言;人工免疫系统;免疫过程建模;建模;群体机器人;

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1. SIMULATING THE IMMUNE INSPIRED ENERGY CHARGING MECHANISM FOR SWARM ROBOTIC SYSTEMS [J] . MOHAMMED AL HAEK, AMELIA RITAHANI ISMAIL Journal of Theoretical and Applied Information Technology . 2017,第20期

机译：群体机器人系统的免疫激发能量模拟
2. An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems [J] . Timmis J., Ismail A. R., Bjerknes J. D., BioSystems . 2016,第Null期

机译：一种自启发式群体机器人系统的免疫群体聚合算法
3. Foraging optimization in swarm robotic systems based on an adaptive response threshold model [J] . Eduardo Castello, Tomoyuki Yamamoto, Yutaka Nakamura, Advanced Robotics: The International Journal of the Robotics Society of Japan . 2014,第19a20期

机译：基于自适应响应阈值模型的群体机器人系统觅食优化
4. Modelling immune systems responses for the development of energy sharing strategies for swarm robotic systems [C] . Al Haek Mohammed, Ismail Amelia Ritahani, Nordin Azlin, International Conference on Computational Science and Technology . 2014

机译：模拟免疫系统对群体机器人系统的能源共享策略发展的反应
5. Multi-robot systems: Modeling swarm dynamics and designing inspection planning algorithms. [D] . Williams, Kjerstin Irja. 2006

机译：多机器人系统：为群体动力学建模并设计检查计划算法。
6. Heteropentameric Cholera Toxin B Subunit Chimeric Molecules Genetically Fused to a Vaccine Antigen Induce Systemic and Mucosal Immune Responses: a Potential New Strategy To Target Recombinant Vaccine Antigens to Mucosal Immune Systems [O] . Tetsuya Harakuni, Hideki Sugawa, Ai Komesu, 2005

机译：基因融合到疫苗抗原的异源性霍乱毒素B亚基嵌合分子诱导全身和粘膜免疫反应：针对重组粘膜免疫系统的疫苗抗原的潜在新策略。
7. Generation and Adaptation Mechanism of Swarm Strategy for Multi-Agent System. Optimization Algorithm of Swarm Strategy by Biologically Inspired Immune Type Swarm Robotic System. [O] . Naoki Mitsumoto, Toshio Fukuda, Fumihito Arai 1995

机译：多代理系统策略战略的生成与适应机制。生物启发免疫型群机械系统的群体策略优化算法。
8. Modeling and Analysis of Stochastic Dynamics and Emergent Phenomena in Swarm Robotic Systems Using the Fokker-Planck Formalism [R] . Kumar, M., Ramakrishnan, S. 2010

机译：基于Fokker-planck形式主义的群体机器人系统随机动力学和应急现象的建模与分析

Modelling immune systems responses for the development of energy sharing strategies for swarm robotic systems

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